System and method for health assessment of downhole tools

ABSTRACT

A system for assessing the health of a mechanism includes a processor for receiving observation data from at least one sensor, the processor including: a detector receptive to the observation data and capable of identifying whether the mechanism is operating in a normal or degraded mode; a diagnoser to identify a type of fault from at least one symptom pattern; and a prognoser capable of calculating a remaining useful life (RUL) of the mechanism, wherein the prognoser includes a population prognoser for calculating the RUL based on a duration of use of the mechanism, a cause prognoser for calculating the RUL based on causal data, and an effect prognoser for calculating the RUL based on effect data generated from the fault. A method and computer program product for assessing the health of a downhole tool is also disclosed.

CROSS REFERENCE TO RELATED APPLICATION

This application is a non-provisional application of U.S. Ser. No.61/047,519, filed Apr. 24, 2008, the contents of which are incorporatedby reference herein in their entirety.

BACKGROUND OF THE INVENTION

Various tools are used in hydrocarbon exploration and production tomeasure properties of geologic formations during or shortly after theexcavation of a borehole. The properties are measured by formationevaluation (FE) tools and other suitable devices, which are typicallyintegrated into a bottomhole assembly. Sensors are used in the FE toolsto monitor various downhole conditions and formation characteristics.

Environments in which FE tools, drilling equipment and other drillstringcomponents operate are very severe, and include conditions such as highdown-hole temperatures (e.g., in excess of 200° C.) and high impactvibration events. Furthermore, rig operators are currently using thetools to perform mission profiles that have previously been impossible,thereby increasing the stress on the tools. Simultaneously, customersare demanding high reliability to help them prevent costly down-holefailures.

To date, periodic maintenance has been the most widely spread method bywhich tool reliability is maintained. As time progresses, there has beena shift toward condition based maintenance, which, as of today, usesdesign guidelines and rough thresholds for nominal operation to assessindividual tool health. Present techniques, however, are inferior inthat a large amount of telemetry data collected during operation thathas yet to be effectively harnessed.

BRIEF DESCRIPTION OF THE INVENTION

A system for assessing the health of a mechanism includes: at least onesensor associated with the mechanism for generating observation data; amemory in operable communication with the at least one sensor, thememory including a database for storing observation data generated bythe sensor; and a processor in operable communication with the memory,for receiving the observation data, the processor including: a detectorreceptive to the observation data and capable of identifying whether themechanism is operating in a normal or degraded mode, the degraded modebeing indicative of a fault in the mechanism; a diagnoser responsive tothe observation data to identify a type of fault from at least onesymptom pattern; and a prognoser in operable communication with the atleast one sensor, the detector and the diagnoser, the prognoser capableof calculating a remaining useful life (RUL) of the mechanism based oninformation from at least one of the sensor, the detector and thediagnoser, wherein the prognoser includes a population prognoser forcalculating the RUL based on a duration of use of the mechanism, a causeprognoser for calculating the RUL based on causal data, and an effectprognoser for calculating the RUL based on effect data generated fromthe fault.

A method for assessing the health of a mechanism includes: receivingobservation data generated by at least one sensor associated with themechanism; identifying whether the mechanism is operating in a normal ordegraded mode, the degraded mode being indicative of a fault in themechanism; and responsive to an identification of the degraded mode,identifying a type of fault from at least one symptom pattern, andcalculating a remaining useful life (RUL) of the mechanism based on acomparison of the observation data with exemplar degradation dataassociated with the type of fault, wherein calculating the RUL is basedon: a duration of use of the mechanism, causal data, and effect datagenerated from the fault.

A computer program product is stored on machine readable media forassessing the health of a mechanism by executing machine implementedinstructions. The instructions perform: receiving observation datagenerated by at least one sensor associated with the mechanism;identifying whether the mechanism is operating in a normal or degradedmode, the degraded mode being indicative of a fault in the mechanism;and responsive to an identification of the degraded mode, identifying atype of fault from at least one symptom pattern, and calculating aremaining useful life (RUL) of the mechanism based on a comparison ofthe observation data with exemplar degradation data associated with thetype of fault.

BRIEF DESCRIPTION OF THE DRAWINGS

The following descriptions should not be considered limiting in any way.With reference to the accompanying drawings, like elements are numberedalike:

FIG. 1 depicts an embodiment of a well logging system;

FIG. 2 depicts an embodiment of a system for assessing the health of adownhole tool;

FIG. 3 is a block diagram of another embodiment of the system of FIG. 2;

FIG. 4 is a flow chart providing an exemplary method for training modelsof the system of FIG. 3;

FIG. 5 is a block diagram of a portion of the system of FIG. 2 forgenerating an estimated observation;

FIG. 6 is a block diagram of a portion of the system of FIG. 2 forgenerating an alarm indicative of a fault;

FIG. 7 is a block diagram of a portion of the system of FIG. 2 forgenerating a symptom observation;

FIG. 8 is a block diagram of a portion of the system of FIG. 2 forgenerating a fault class estimate;

FIG. 9 is a block diagram of a portion of the system of FIG. 2 forgenerating a degradation path and an associated lifetime;

FIG. 10 is a block diagram of a portion of the system of FIG. 2 forgenerating an estimate of a remaining useful life of the downhole tool;

FIG. 11 illustrates exemplar degradation paths;

FIG. 12 illustrates an observed degradation path and the exemplardegradation paths of FIG. 11;

FIG. 13 is a flow chart providing an exemplary method for classifying adegradation path and estimating the RUL associated with the degradationpath; and

FIG. 14 depicts an alternative embodiment of a system for assessing thehealth of a downhole tool.

DETAILED DESCRIPTION OF THE INVENTION

There is provided a system and method for assessing the health of adownhole tool or other mechanism. The method is a data driven approachfor assessing the health of bore hole assembly tools. The methodincludes analyzing data retrieved from a formation evaluation (FE) toolor other downhole device to determine: 1) whether or not there is afault in the device, 2) if there is a fault, the type of fault, and 3) aremaining useful life (RUL) of the tool. In one embodiment, the methodincludes comparing collected telemetry data and associated statistics todata driven models that have been trained to: 1) differentiate betweennominal and degraded operation for fault detection, 2) differentiatebetween a series of possible fault classes for diagnosis, and 3)differentiate between similar and dissimilar degradation paths forprognosis (i.e. the estimation of the remaining useful life).

A detailed description of one or more embodiments of the disclosedsystem and method are presented herein by way of exemplification and notlimitation with reference to the Figures. Additional description of thesystem and method are provided by the following publications: 1) DustinR. Garvey and J. Wesley Hines, “Data Based Fault Detection, Diagnosis,and Prognosis of Oil Drill Steering Systems”, The University ofTennessee, Knoxville, Department of Nuclear Engineering, and 2) J.Wesley Hines and Dustin R. Garvey, “Monitoring, Diagnostics, andPrognostics for Drilling Operations: Final Report”, August 2007, NuclearEngineering Department, University of Tennessee, both or which arehereby incorporated by reference in their entirety.

Referring to FIG. 1, an exemplary embodiment of a well logging system 10includes a drillstring 11 that is shown disposed in a borehole 12 thatpenetrates at least one earth formation 14 for making measurements ofproperties of the formation 14 and/or the borehole 12 downhole. Drillingfluid, or drilling mud 16 may be pumped through the borehole 12. Asdescribed herein, “formations” refer to the various features andmaterials that may be encountered in a subsurface environment.Accordingly, it should be considered that while the term “formation”generally refers to geologic formations of interest, that the term“formations,” as used herein, may, in some instances, include anygeologic points or volumes of interest (such as a survey area). Inaddition, it should be noted that “drillstring” as used herein, refersto any structure suitable for lowering a tool through a borehole orconnecting a drill to the surface, and is not limited to the structureand configuration described herein.

In one embodiment, a bore hole assembly (BHA) 18 is disposed in the welllogging system 10 at or near the downhole portion of the drillstring 11.The BHA 18 includes any number of downhole formation evaluation (FE)tools 20 for measuring versus depth and/or time one or more physicalquantities in or around a borehole. The taking of these measurements isreferred to as “logging”, and a record of such measurements is referredto as a “log”. Many types of measurements are made to obtain informationabout the geologic formations. Some examples of the measurements includegamma ray logs, nuclear magnetic resonance logs, neutron logs,resistivity logs, and sonic or acoustic logs.

Examples of logging processes that can be performed by the system 10include measurement-while-drilling (MWD) and logging-while-drilling(LWD) processes, during which measurements of properties of theformations and/or the borehole are taken downhole during or shortlyafter drilling. The data retrieved during these processes may betransmitted to the surface, and may also be stored with the downholetool for later retrieval. Other examples include logging measurementsafter drilling, wireline logging, and drop shot logging.

The downhole tool 20, in one embodiment, includes one or more sensors orreceivers 22 to measure various properties of the formation 14 as thetool 20 is lowered down the borehole 12. Such sensors 22 include, forexample, nuclear magnetic resonance (NMR) sensors, resistivity sensors,porosity sensors, gamma ray sensors, seismic receivers and others.

Each of the sensors 22 may be a single sensor or multiple sensorslocated at a single location. In one embodiment, one or more of thesensors includes multiple sensors located proximate to one another andassigned a specific location on the drillstring. Furthermore, in otherembodiments, each sensor 22 includes additional components, such asclocks, memory processors, etc.

In one embodiment, the tool 20 is equipped with transmission equipmentto communicate ultimately to a surface processing unit 24. Suchtransmission equipment may take any desired form, and differenttransmission media and methods may be used. Examples of connectionsinclude wired, fiber optic, wireless connections or mud pulse telemetry.

In one embodiment, the surface processing unit 24 and/or the tool 20include components as necessary to provide for storing and/or processingdata collected from the tool 20. Exemplary components include, withoutlimitation, at least one processor, storage, memory, input devices,output devices and the like. The surface processing unit 24 optionallyis configured to control the tool 20.

In one embodiment, the tool 20 also includes a downhole clock 26 orother time measurement device for indicating a time at which eachmeasurement was taken by the sensor 20. The sensor 20 and the downholeclock 26 may be included in a common housing 28. With respect to theteachings herein, the housing 28 may represent any structure used tosupport at least one of the sensor 20, the downhole clock 26, and othercomponents.

Referring to FIG. 2, there is provided a system 30 for assessing thehealth of the downhole tool 20, or other device used in conjunction withthe BHA 18 and/or the drillstring 11. The system may be incorporated ina computer or other processing unit capable of receiving data from thetool. The processing unit may be included with the tool 20 or includedas part of the surface processing unit 24.

In one embodiment, the system 30 includes a computer 31 coupled to thetool 20. Exemplary components include, without limitation, at least oneprocessor, storage, memory, input devices, output devices and the like.As these components are known to those skilled in the art, these are notdepicted in any detail herein. The computer 31 may be disposed in atleast one of the surface processing unit 24 and the tool 20.

Generally, some of the teachings herein are reduced to an algorithm thatis stored on machine-readable media. The algorithm is implemented by thecomputer 31 and provides operators with desired output.

The tool 20 generates measurement data, which is stored in a memoryassociated with the tool and/or the surface processing unit. Thecomputer 31 receives data from the tool 20 and/or the surface processingunit for health assessment of the tool 20. Although the computer 31 isdescribed herein as separate from the tool 20 and the surface processingunit 24, the computer 31 may be a component of either the tool 20 or thesurface processing unit 24, and accordingly either the tool 20 or thesurface processing unit 24 may serve as an apparatus for assessing toolhealth.

Referring to FIG. 3, the system 30 includes a memory 32 in which one ormore databases 34, 36 and 38 are stored. The system 30 also includes aprocessor 40, which includes one or more analysis units includingempirical models 42, 44, 46 and 48. The models described herein are datadriven models, i.e. the data describing input and output characteristicsdefines the model.

The data used by the system 30 is a plethora of data that describedifferent aspects of how individual tools within a fleet perform, areused, and in some cases fail. In one embodiment, the data associatedwith a selected tool 20 is categorized into three main types. The typesof data include memory dump data 34, operational data 36, andmaintenance data 38.

Memory dump data 34 is a collection and/or display of the contents of amemory associated with the tool 20. Memory dump data 34 includes, forexample, sensor readings related to sensed physical quantities in and/oraround the borehole, such as temperature, pressure and vibration.Operational data 36 includes measurements relating to the operation ofthe tool, such as electrical current and motor or drill rotation.Maintenance data 38 includes data retrieved from the tool after a faultis observed.

The predictor 42 and the detector 44 are used to determine whether thetool 20 is operating in either a nominal (i.e., normal) or degradedmode. The predictor 42 produces estimates of measured observations andgenerates estimate residuals based on comparison with exemplarobservations, and the detector 44 evaluates whether the tool isoperating in a degraded mode based on the estimate residuals. Thediagnoser 46 is used to identify the type or class of any detectedfaults from symptom patterns generated from the observations. Symptompatterns include, but are not limited to, predictor estimate residuals,alarm patterns, and signals that can be used to quantify environmentalor operational stress. The prognoser 48 is used to infer the remaininguseful life (RUL) of the tool 20 from observations of its degradationpath or history.

In one embodiment, the system is a nonparametric fuzzy inference system(NFIS). The NFIS is a fuzzy inference system (FIS) whose membershipfunction centers and parameters are observations of exemplar inputs andoutputs.

In one embodiment, prior to utilizing the system 30 for assessing toolhealth, the models 42, 44, 46, 48 are trained based on un-faulted datato be able to detect faults, diagnose the faults and determine remaininguseful life. This training, in one embodiment, is performed via trainingprocedure 50.

FIG. 4 illustrates a method, i.e., a training procedure 50, for trainingthe models in system 10. The method 50 includes one or more stages 51,52, 53 and 54. In one embodiment, the method 50 includes the executionof all of stages 51, 52, 53 and 54 in the order described. However,certain stages may be omitted, stages may be added, or the order of thestages changed.

In the first stage 51, the predictor 42 is trained by building a casebase in the predictor 42 memory. The predictor's case base is built byselecting a number of exemplar observations, referred to as “ExampleObs. #1-#Np” in FIG. 3, from signals collected from un-faulted tooloperation. These signals, in one embodiment, are collected from memorydump data 34. As used herein, the term “signal” or “observation” refersto measurement, operations or maintenance data received for the tool 20.Each signal, in one embodiment, consists of one or more data points overa selected time interval.

In one embodiment, each signal may be processed using methods thatinclude statistical analysis, data fitting, and data modeling to producean observation curve. Examples of statistical analysis includecalculation of a summation, an average, a variance, a standarddeviation, t-distribution, a confidence interval, and others. Examplesof data fitting include various regression methods, such as linearregression, least squares, segmented regression, hierarchal linearmodeling, and others.

In the second stage 52, the detector 44 is trained by calculating aresidual for each observation by calculating an error between themeasured values of the observation and predicted values. Each residualis passed to a statistical routine to construct a number of distributionfunctions for each residual, such as probability distribution functions(PDFs), that are representative of nominal system operation. Theseexemplar nominal distribution functions are represented as “NominalDist. #P” in FIG. 3, where “P” refers to the number of residual signals.

In the third stage 53, the results of predictor and detector trainingare combined with selected signal, operations, and maintenance data tocreate the diagnoser's case base that will be used to map symptompatterns to fault classes.

In this stage, data such as the residuals are extracted from one or moreof the databases 32, 34, 36 to create the symptom patterns associatedwith a known fault type, i.e. fault class. These symptom patterns arethen consolidated and included as exemplars in the diagnoser 46. At thispoint, the diagnoser 46 has effectively learned the relationship betweenthe estimate residuals and known fault classes.

In the fourth stage 54, analysis results from previous stages arecombined with additional signal, operations, and maintenance data tocreate the prognoser's case base that maps degradation paths, such asabsorbed vibration, to tool life. Degradation paths utilize data pointsfrom the predictor 42, detector 44 and diagnoser 46, such as observationdata and alarm data over a time interval including the time that thetool 20 failed. Additional information from the memory dump data 34 mayalso be combined, such as additional signals or composed signals (ex.running sum above a threshold), to create the degradation paths. Anysuitable regression functions or data fitting techniques may be appliedto the data retrieved from the tool 20 to generate the degradation path.

FIGS. 5-10 illustrate methods for assessing the health of a downholetool or other component of a formation evaluation/exploration system,such as a tool used in conjunction with a drillstring to perform adownhole measurement. The methods include various stages describedherein. The methods may be performed continuously or intermittently asdesired. The methods are described herein in conjunction with thedownhole tool 20, although the methods may be performed in conjunctionwith any number and configuration of sensors and tools, as well as anydevice for lowering the tool and/or drilling a borehole. The methods maybe performed by one or more processors or other devices capable ofreceiving and processing measurement data, such as the computer 31. Inone embodiment, the method includes the execution of all of stages inthe order described. However, certain stages may be omitted, stages maybe added, or the order of the stages changed.

Referring to FIG. 5, in the first stage, tool dump data 34, or otherdata collected from the tool or other component of the well loggingsystem 10, is collected from the tool's memory to extract usefulinformation. From that data, a number of query observations 58 (“Obs.#1-#NQ), i.e., measured observations, are entered into the predictor 42.

In one embodiment, query observations 58 include any type of datarelating to measured characteristics of the formation and/or borehole,as well as data relating to the operation of the tool. In one example,the data includes pressure, electric current, motor RPM, drill rotationrate, vibration and temperature measurements.

The predictor 42 calculates estimated observations 60 (“Estimate Obs.#1-#NQ), by determining which of the predictor's exemplar observationsare most similar to each observed query observation 60.

In one embodiment, the predictor 42 is an NFIS predictor. Thisembodiment of the predictor 42 is a nonparametric, autoassociative modelthat performs signal correction through correlations inherent in thesignals. This embodiment reduces the effects of noise or equipmentanomalies and produces signal patterns similar to those from normaloperating conditions. In another embodiment, the predictor 42 is anautoassociative kernel regression (AAKR) predictor.

Because the predictor 42 has been previously trained on exclusively“good” data, i.e., data generated during known nominal operation, thepredictor 42 effectively learns the correlations present during nominal,un-faulted tool operation. So when these correlations change, which isoften the case when a fault is present, the predictor 42 is still ableto estimate what the signal values should be, had there not been achange in correlation. Thus, the system 30 provides a dynamic referencepoint that can be compared to measured observations, in that as soon asthere is a change in the signal correlations, there will be acorresponding divergence of the estimates from the observations.Generally, when a fault is present in the well logging system 10, theestimates will generally be far from their observed values for theaffected signals.

In one embodiment, the predictor 42 utilizes various regression methods,including nonparametric regression such as kernel regression, togenerate an estimate observation 60 that corresponds to a queryobservation 58. Kernel regression (KR) includes estimating the value bycalculating a weighted average of historic, exemplar observations. Themethods herein are not limited to any particular statistical analysis,as any methods, such as curve fitting, may be used.

For example, for a number of exemplar observations, KR estimation isperformed by calculating a distance “d” of a query observation, i.e.,input “x”, from each of the exemplar observations “X_(i)”, inputting thedistances into a kernel function which converts the distances toweights, i.e., similarities, and estimating the output by calculating aweighted average of an output exemplar.

The distance may be calculated via any known technique. One example of adistance is a Euclidean distance, represented by the following equation:d(X _(i) ,x)=X _(i) −x,where “i” represents a number of inputs. Another example of distance isthe adaptive Euclidean distance, in which distance calculation isexcluded for those measured observations that lie outside the range ofthe maximum and minimum input exemplars.

To transform the distance d into a weight or similarity, in oneembodiment, a kernel function “K_(h)(d)” is used. An example of such akernel function is the Gaussian kernel, which is represented by thefollowing equation:

${{K_{h}(d)} = {\frac{1}{\sqrt{2\pi\; h^{2}}}{\mathbb{e}}^{{{- d^{2}}/2}h^{2}}}},$where “h” refers to the kernel's bandwidth and is used to control whateffective distances are deemed similar. Other exemplary kernel functionsinclude the inverse distance, exponential, absolute exponential, uniformweighting, triangular, biquadratic, and tricube kernels.

In one embodiment, the calculated similarities of the query input x arecombined with each of the exemplar values X_(i) to generate estimates ofthe output, i.e., estimated observations 60. This is accomplished, in KRfor example, by calculating a weighted average of the output exemplarsusing the similarities of the query observation to the input exemplarsas weighting parameters, as shown in the following equation:

${{\hat{y}(x)} = \frac{\sum\limits_{i = 1}^{n}\left\lbrack {{K\left( {X_{i} - x} \right)}Y_{i}} \right\rbrack}{\sum\limits_{i = 1}^{n}{K\left( {X_{i} - x} \right)}}},$where “n” is the number of exemplar observations in the KR model,“X_(i)” and “Y_(i)” are the input and output for the i^(th) exemplarobservation, x is a query input, K(X_(i)−x) is the kernel function, andŷ(x) is an estimate of y, given x.

In one embodiment, varying numbers and types of inputs and outputs maybe analyzed using different KR architectures. The variables and inputsdescribed herein, in one embodiment, are represented by vectors whenmultiple inputs are used. For example, an inferential KR model usesmultiple inputs to infer an output, a heteroassociative KR model usesmultiple inputs to predict multiple outputs, and an autoassociative KR(AAKR) model uses inputs to predict the “correct” values for the inputs,where “correct” refers to the relationships and behaviors contained inthe exemplar observations.

Referring to FIG. 6, in the second stage, the estimated observations 60are used to determine whether a fault has occurred. A number ofresiduals 62 corresponding to the number “N_(Q)” of observations 58 arecalculating by subtracting each estimate observation 60 from acorresponding query observation 58. The resulting residual observations62 each have a value that represents a change in correlation from theun-faulted observation.

Each residual observation 62 is then passed to the detector 44 whichuses a statistical test to determine whether the current sequence ofresidual observations 62 is more likely to have been generated from anominal mode (meaning that there is no fault) or a degraded mode(meaning that there is a fault). In one embodiment, the residualobservations 62 are evaluated by a cumulative sum (CUSUM) or sequentialprobability ratio test (SPRT) statistical detector, to determine if thetool is operating in a nominal or degraded mode.

In one embodiment, threshold values for determining whether the tool 20is operating in a degraded mode are determined. In one example, thenominal mode is defined during training, and a number of degraded modesare enumerated with respect to the nominal mode. Each degraded modecorresponds to a selected threshold. For example, mean upshift and meandownshift degraded modes are defined by offsetting the nominaldistribution to a higher and lower mean value, respectively. A series oftests is then performed to indicate which distribution the sequence ismost likely to have been generated by.

In one embodiment, a sequential analysis such as a sequentialprobability ratio test (SPRT) is performed to determine whether theresidual observation 62 is resulting from nominal mode operation ordegraded mode operation. SPRT is used to determine whether a sensor ismore likely in a nominal mode, “H₀”, or in a degraded mode, “H₁”. SPRTincludes calculating a likelihood ratio, “L_(n)”, shown in the followingequation:

$\quad\begin{matrix}{L_{n} = \frac{{probability}{\mspace{11mu}\;}{of}\mspace{14mu}{observing}\mspace{14mu}\left\{ x_{n} \right\}\mspace{14mu}{given}\mspace{14mu} H_{1}\mspace{14mu}{is}\mspace{14mu}{true}}{{probability}{\mspace{11mu}\;}{of}\mspace{14mu}{observing}\mspace{14mu}\left\{ x_{n} \right\}\mspace{14mu}{given}\mspace{14mu} H_{0}\mspace{14mu}{is}\mspace{14mu}{true}}} \\{= \frac{p\left( {\left\{ x_{n} \right\}/H_{1}} \right)}{p\left( {\left\{ x_{n} \right\}/H_{0}} \right)}}\end{matrix}$where {x_(n)} is a sequence of consecutive “n” observations of x. Thelikelihood ratio is then compared to a lower (A) and upper (B) bound, asthose defined by a false alarm probability (α) and a missed alarmprobability (β) shown in the following equations:

$A = \frac{\beta}{1 - \alpha}$ $B = \frac{1 - \beta}{\alpha}$

If the likelihood ratio is less than A, the residual observation 62 isdetermined to belong to the system's normal mode H₀. If the likelihoodratio is greater than B, the residual observation 62 is determined tobelong to the system's degraded mode H₁ and a fault is registered.

If any test outcome indicates that the residuals are not likely to havebeen generated from the nominal mode, the detector 44 generates an alarm64, which indicates that a fault in the tool 20 has potentiallyoccurred. Such alarms 64 are referred to as “Alarm Obs. #1-#N_(Q)”, andmay be any number of alarms 64 between zero and NQ.

If the output of the detector 44 indicates that the tool 20 is operatingnormally (i.e., no fault or anomaly has occurred), then no maintenanceor control action is performed and the system 30 examines the nextobservation. However, if the detector 44 indicates that the tool 20 isoperating in a degraded mode, the prediction and detection results arepassed to the diagnoser 46, which maps provided symptom patterns 66(i.e. prediction residuals, signals, alarms, etc.) to known faultconditions to determine the nature of the fault.

Referring to FIG. 7, in the third stage, symptom patterns 66 are createdby the processor 40 that encapsulate a sufficient amount of informationto differentiate between the identified faults. The symptom patterns 66are referred to as “Symptom Obs. #1-N_(QS)” in FIG. 7, where “N_(QS)” isa number less than or equal to NQ. The symptom patterns 66 arecalculated by combining the data from predictor 42 and detector 44,including one or more of the query observations 58, estimateobservations 60, residual observations 62 and alarms 64 for each signal.In one embodiment, additional information from the memory dump data 34,such as additional signals or a synthesis of additional signals, and/orsignals that can be used to quantify environmental or operationalstress, is also combined with the data from the predictor 42 and thedetector 44 to create the symptom observations 66.

In one embodiment, the residual observations 62, optionally incombination with the alarms 64, are provided as the symptom patterns 66.Examples of symptom patterns 66 include measured hydraulic unit signalvalues alone and with associated residuals, stick-slip signals (i.e., arate by which a drill rotates in its shaft) with associated estimateresiduals, and vibration signals with associated estimate residuals.

Referring to FIG. 8, in the fourth stage, the observations, associatedalarms and residuals are entered in the diagnoser 46. In one embodiment,the diagnoser 46 is an NFIS diagnoser. In another embodiment, only datarelated to observations that generate an alarm 64 are entered in thediagnoser 46.

In one embodiment, the symptom observations 66 are entered into thediagnoser 46, which infers the class or type of fault for each symptomobservation 66. Classification of the class (i.e. class “A”-“Z”) isperformed by comparing the symptom observations 66 to exemplar symptompatterns previously generated by the diagnoser 46, and then combiningthe results of this comparison with each exemplar symptom pattern togenerate an estimate 68 of the class. In one embodiment, each symptomobservation 66 is compared to the symptom patterns, and is assigned aclass that is associated with the symptom pattern to which it is mostsimilar. This class estimate 68, referred to as “Class Estimate Obs.#1-#NQS” in FIG. 8, is produced for each observation 58 that exhibits afault. In one embodiment, the frequency of the classes (e.g., class A,class B, etc.) in the estimate observations 60 is determined to obtain afinal diagnosis for the tool 20 and/or its components.

Faults may occur for any of various reasons, and associated faultclasses are designated. Examples of fault classes include “Mud invasion”(MI), in which drilling mud 16 enters a tool 20 and causes failure,“pressure transducer offset” (PTO), in which sensor offset (negative andpositive) causes problems in the control of the system 10 whicheventually results in system failure, and “pump startup” (PS), in whicha pump fails after the drill is started.

In one embodiment, “nearest neighbor” (NN) classification is utilized todetermine which class a symptom observation 66 falls into, whichinvolves assigning to an unclassified sample point the classification ofthe nearest of a set of previously classified points. An example ofnearest neighbor classification is k-nearest neighbor (kNN). kNN refersto the classifier that examines the number “k” of nearest neighbors of aquery pattern, and NN refers to the classifier that examines the closestneighbor (i.e. k=1). NN classification includes calculating a distancebetween a query pattern and each exemplar symptom pattern, andassociating the query pattern with a class that is associated with theexemplar symptom pattern having the smallest distance.

kNN classification includes calculating the distances for each exemplarsymptom pattern, sorting the distances, and extracting the outputclasses for the k smallest distances. The number of instances of eachclass represented by the k smallest distances is counted, and the classof the query pattern is designated as the class with the largestrepresentation in the k nearest neighbors.

An example of nearest neighbor classification is described herein. Inthis example, a number “n” of exemplar symptom patterns are collectedfor “p” inputs (i.e., variables) that are examples of a number “n_(c)”classes. Also, “C_(i)” designates the i^(th) class and “n_(i)”designates the number of examples for a class. Using these definitions,the sum of the number of examples for each class is equal to the numberof exemplar symptom patterns.

In this example, the training inputs (i.e., exemplar symptom patterns)are denoted by X and the outputs (i.e., classes) are denoted by Y.“Memory” matrices or vectors are created for the inputs and outputs asfollows:

$X = {{\begin{bmatrix}X_{1,1} & \ldots & X_{1.p} \\\vdots & \; & \vdots \\X_{n_{1}{.1}} & \ldots & X_{n_{1}.p} \\X_{n_{1} + 1.1} & \ldots & X_{n_{1} + {1.p}} \\\vdots & \; & \vdots \\X_{{n_{1} + n_{2}},1} & \ldots & X_{n_{1} - {n_{2}.p}} \\\vdots & \; & \vdots \\{X_{{n_{1} + \ldots + n_{c - 1}},1}} & \ldots & {X_{{n_{1} + \ldots + n_{c - 1}},p}} \\\vdots & \; & \vdots \\X_{n{.1}} & \ldots & X_{n.p}\end{bmatrix}\mspace{14mu} Y} = \begin{bmatrix}C_{1} \\\vdots \\C_{1} \\C_{2} \\\vdots \\C_{2} \\\vdots \\{C_{n_{c}}} \\\vdots \\{C_{n_{c}}}\end{bmatrix}}$

Classification of a query observation of the p inputs, which is denotedby x, is performed. The query observation x is represented by thefollowing equation:x=[x ₁ . . . x _(p)]

The distance, such as the Euclidean distance, can be used to determinehow close the query observation is to each of the input exemplars. Inequation form, the distance of the query to the i^(th) example is givenby:d(X _(i) ,x)=√{square root over ((X _(i,1) −x ₁)²+(X _(i,2) −x ₂)²+ . .. +(X _(i,p) −x _(p))²)}{square root over ((X _(i,1) −x ₁)²+(X _(i,2) −x₂)²+ . . . +(X _(i,p) −x _(p))²)}{square root over ((X _(i,1) −x ₁)²+(X_(i,2) −x ₂)²+ . . . +(X _(i,p) −x _(p))²)}

The distance calculation is repeated for the n exemplars, the result isa vector of n distances:

$d = {\begin{bmatrix}{d\left( {X_{1},x} \right)} \\{d\left( {X_{2},x} \right)} \\\vdots \\{d\left( {X_{n},x} \right)}\end{bmatrix}.}$

To classify x with the nearest neighbor classifier, the output orclassification is the example class that corresponds to the minimumdistance.

The types of classification methods used herein are merely exemplary.Any number or type of technique may be used for comparing data patternsfrom a sensor or sensor to known data patterns for fault classificationmay be used.

Referring to FIG. 9, in the fifth stage, a degradation path 70 andassociated lifetime 72 is calculated for each signal. The degradationpaths 70 are referred to as “Degradation Path #1-#N_(QD)” and thelifetimes 72 are referred to as “Lifetime #1-#N_(QD)”, where N_(QD) isthe number of degradation paths 70. From this data, the remaining usefullife of the tool can be calculated. The degradation path 70 is createdby combining the data from the predictor 42, detector 44 and diagnoser46, including one or more of the signal observations 58, signalestimates 60, estimate residuals 62, alarms 64, symptom observations 66,and class estimates 68. Additional information from the memory dump data34 may also be combined, such as additional signals or composed signals(ex. running sum above a threshold), to create the degradation paths.Any suitable regression functions or data fitting techniques may beapplied to the data retrieved from the tool to generate the degradationpath. Many types of statistical analyses are utilized to calculate thedegradation path, such as polynomial regression, power regression, etc.for simple data relationships, and utilizing fuzzy inference systems,neural networks, etc. for complex relationships.

The degradation path 70 may be generated from any desired measurementdata. Examples of such data used for degradation paths include:drillstring crack length, measured pressure, electrical current, motorand/or drill rotation and temperature over a selected time period.

Lifetimes 72 that correspond to each degradation path 70 are generated.In one embodiment, a threshold value may be set for degradation path 70,indicating a failure. This threshold may be based on extrapolation ofdata from the existing degradation path 70, or based on pre-existingexemplar degradation paths associated with known failure times.

Referring to FIG. 10, the degradation paths 70 and lifetimes 72 areentered into the prognoser 48 which uses this information to generateestimates of the remaining useful life (RUL) 74 according to each path.The RUL for each path may be referred to as “RUL Estimate #1-#N_(QD)”.In one embodiment, the prognoser 48 is an NFIS prognoser. The querydegradation paths 70 are compared to the exemplar degradation paths, andthe results of the comparison with the exemplar lifetimes are comparedto generate an estimate 74 of the tool 20 and/or component RULs. In oneembodiment, a path classification and estimation (PACE) model thatutilizes an associated PACE algorithm is used to generate the RULestimate 74.

The PACE algorithm is useful for situations in which i) each degradationpath 70 includes a discrete failure threshold that accurately predictswhen a device will fail, and ii) the degradation paths 70 do not exhibita clear failure threshold. In one embodiment, for example, fordegradation paths 70 that exhibit well established thresholds (e.g.,seeded crack growth, and controlled testing environments, such asconstant load or uniform cycling), the data can be formatted such thatthe instant where the degradation path 70 crosses the failure thresholdis interpreted as a failure event.

In other embodiments, a defined discrete failure threshold is not alwaysavailable. In some such embodiments, and indeed in many real worldapplications, where the failure modes are not always well understood orcan be too complex to be quantified by a single threshold, the failureboundary is gray at best.

The PACE algorithm involves two general operations: 1) classify acurrent degradation path 70 as belonging to one or more of previouslycollected exemplar degradation paths and 2) use the resultingmemberships to estimate the RUL.

Referring to FIG. 11, exemplar degradation signals 76 are shown,represented as “Y_(i)(t)”, and their associated time-to-failure(TTF_(i)). In this example, it can be seen that there is not a clearthreshold for the degradation path 70. In one embodiment, the exemplarysignals 76 are generalized by fitting an arbitrary function 78, referredto as “f_(i)(t,θ_(i))”, to the data via regression, machine learning, orother fitting techniques.

In one embodiment, two pieces of information are extracted from thedegradation paths, specifically the TTFs and the “shape” of thedegradation that is described by the functional approximationsf_(i)(t,θ_(i)). These pieces of information can be used to construct avector of exemplar TTFs and functional approximations, as follows:

${T\; T\; F} = \begin{bmatrix}{T\; T\; F_{1}} \\{T\; T\; F_{2}} \\{T\; T\; F_{3}} \\{T\; T\; F_{4}}\end{bmatrix}$ ${f\left( {t,\Theta} \right)} = \begin{bmatrix}{f_{1}\left( {t,\theta_{1}} \right)} \\{f_{2}\left( {t,\theta_{2}} \right)} \\{f_{3}\left( {t,\theta_{3}} \right)} \\{f_{4}\left( {t,\theta_{4}} \right)}\end{bmatrix}$where TTFi and fi(t,θi) are the TTF and functional approximation of thei^(th) exemplar degradation signal path, θi are the parameters of thei^(th) functional approximation of the i^(th) exemplar degradationsignal path, and Θ are all of the parameters of each functionalapproximation.

In one embodiment, the degradation path is calculated using a GeneralPath Model (GPM). The GPM involves parameterizing a device's degradationsignal to calculate the degradation path and determine the TTF. In oneembodiment, the TTF may be described as a probability of failuredepending on time. The TTF may be set at any selected probability offailure.

In one embodiment, generic PDFs are fit to a degradation signal tomeasure the degradation path and TTF. For example, if N devices arebeing tested and N_(T) is the total number of devices that have failedup to the current time T, then the fraction of devices that have failedcan be interpreted as the probability of failure for all times less thanor equal to the current time. More specifically, the cumulativeprobability of failure at time T, designated by P(T≦t), is the ratio ofthe current number of failed devices (NT) to the total number of devices(N), as shown in the following equation:

${P\left( {T \leq t} \right)} = {\frac{N_{T}}{N}.}$

If a generic probability density function (PDF) is fit to observedfailure data, then the above equation can be written in terms of a PDF,referred to as “f(t)” and its associated continuous distributionfunction (CDF), referred to as “F(t)”:

P(T ≤ t) = F(t) = ∫₀^(t)f(t^(′)) 𝕕t^(′).

The above equation can also be used to define the probability that afailure has not occurred for all times less than the current time t,referred to as the reliability function “R(t)”:

R(t) = 1 − F(t) = ∫_(t)^(∞)f(t^(′)) 𝕕t^(′).

In one embodiment, additional reliability metrics are calculated usingTTF distribution data and the reliability functions to predict andmitigate failure, namely the mean time-to-failure (MTTF) and the 100pthpercentile of the reliability function. MTTF characterizes the expectedfailure time for a sample device drawn from a population. The followingequation can be used to calculate the MTTF for a continuous TTFdistribution:

M T T F = ∫₀^(∞)tf(t) 𝕕t,and can be further defined in terms of the reliability function:

M T T F = ∫₀^(∞)R(t) 𝕕t.

In one embodiment, as an alternative to the MTTF, the 100pth percentileof the reliability function is used to determine the time (tp) at whicha specified fraction of the devices have failed. In equation form, thetime at which 100p % of the devices have failed is simply the time atwhich the reliability function has a value of p:R(t _(p))=1−p,where p has a value between zero and one.

Referring to FIG. 12, the RUL is calculated for an observed degradationpath 70. The degradation path 70 has a value “y(t*)” of the degradationpath 70 at a time “t*”. To estimate the RUL of the device via the PACEmodel, the algorithm presented in FIG. 13 is utilized.

Referring to FIG. 13, in one embodiment, an exemplary method 80 forestimating the RUL includes any number of stages 81-83.

In the first stage 81, the expected degradation signal values accordingto the exemplar degradation paths 76 are estimated by evaluating theregressed functions at t*. The current time t* is used to estimate theexpected values of the degradation path 70 according to the exemplarpaths 76. In one embodiment, the expected values of the degradation path70 according to the exemplar paths 76 are the approximating functions 78evaluated at the time t*, as shown in the following equation:

${f\left( {t^{*},\Theta} \right)} = {\begin{bmatrix}{f_{1}\left( {t^{*},\theta_{1}} \right)} \\{f_{2}\left( {t^{*},\theta_{2}} \right)} \\{f_{3}\left( {t^{*},\theta_{3}} \right)} \\{f_{4}\left( {t^{*},\theta_{4}} \right)}\end{bmatrix}.}$

The values of the above function evaluations can be interpreted asexemplars of the degradation path 70 at time t*. In this context, theabove vector can be rewritten as a follows:

${Y\left( t^{*} \right)} = {\begin{bmatrix}{f_{1}\left( {t^{*},\theta_{1}} \right)} \\{f_{2}\left( {t^{*},\theta_{2}} \right)} \\{f_{3}\left( {t^{*},\theta_{3}} \right)} \\{f_{4}\left( {t^{*},\theta_{4}} \right)}\end{bmatrix} = \begin{bmatrix}{Y_{1}\left( t^{*} \right)} \\{Y_{2}\left( t^{*} \right)} \\{Y_{3}\left( t^{*} \right)} \\{Y_{4}\left( t^{*} \right)}\end{bmatrix}}$

In stage 82, the expected RULs are calculated by subtracting the currenttime t* from the observed TTFs of the exemplar paths 76. This is shown,for example, in the following equation:

${R\; U\;{L\left( t^{*} \right)}} = {{{T\; T\; F} - t^{*}} = \begin{bmatrix}{{T\; T\; F_{1}} - t^{*}} \\{{T\; T\; F_{2}} - t^{*}} \\{{T\; T\; F_{3}} - t^{*}} \\{{T\; T\; F_{4}} - t^{*}}\end{bmatrix}}$

In stage 83, the observed degradation path 70 at time t*, y(t*), isclassified based on a comparison with the expected degradation signalvalues Y(t*). The degradation path 70 is classified as belonging to theclass associated with the exemplar path 76 to which it is closest invalue. In one embodiment, the signal value y(t*) can be compared to theexpected degradation signal values Y(t*) by any one of a number ofclassification algorithms to obtain a vector of membershipsμ_(γ)[y(t*)]. In this embodiment, the memberships have values of zero orone and μ_(γi)[y(t*)] denotes the membership of y(t*) to the i^(th)exemplar path, as shown in the following equation:

${µ_{Y}\left\lbrack {y\left( t^{*} \right)} \right\rbrack} = {\begin{bmatrix}{\mu_{Y_{1}}\left\lbrack {y\left( t^{*} \right)} \right\rbrack} \\{\mu_{Y_{2}}\left\lbrack {y\left( t^{*} \right)} \right\rbrack} \\{\mu_{Y_{3}}\left\lbrack {y\left( t^{*} \right)} \right\rbrack} \\{\mu_{Y_{4}}\left\lbrack {y\left( t^{*} \right)} \right\rbrack}\end{bmatrix}.}$

The vector of memberships of the signal value y(t*) to the exemplardegradation paths 76 is combined with the vector of expected RULs toestimate the RUL of the individual device.

In one embodiment, the estimate of the RUL of a device is generated byapplying one or more of multiple types of prognosers, including apopulation prognoser to estimate the RUL from population based failurestatistics, and individual prognosers including a causal prognoser toestimate the RUL by monitoring the causes of component faults/failures(e.g. by examining stressor signals such as vibration, temperature,etc.), and an effect prognoser to estimate the RUL by examining theeffect of component fault/failure on the individual device by examiningthe output of a monitoring system. In one embodiment, multiple effectprognosers are provided to estimate the RUL for each fault class.

In one example, the causal prognoser utilizes absorbed vibration energydata to estimate the RUL by examining the cause of failure. In anotherexample, the effect prognoser calculates a cumulative sum of the alarms64 is used to estimate the RUL by examining the effect of the onset offailure.

In one example, the population prognoser is continuously used toestimate the RUL by calculating the expected RUL given the currentamount of time that the device has been used. In addition, stressorsignal data (e.g., vibration, temperature, etc.) is used as inputs tothe causal prognosers for each of the identified effects, whichestimates the RUL by examining the amount of stress absorbed by thedevice. Similarly, relevant signal data is also extracted from thecollected device data and used as inputs to a monitoring system, whichdetermines whether the device is currently operating in a nominal ordegraded mode. If the monitoring system infers that the device isoperating in a degraded mode, then the original signals and monitoringsystem outputs are used as inputs to a diagnosis system thatsubsequently selects the appropriate effect prognoser based on theobserved patterns. For example, if the diagnoser 46 classifies thecurrent operation of the device as being representative of the i^(th)fault class, then the i^(th) effect prognoser will be used to estimatethe RUL.

Referring to FIG. 14, an alternative exemplary system 80 includes adevice database 82, a monitor 84, a diagnosis system 86, a populationprognoser 88, a MI cause prognoser 90, a PTO cause prognoser 92, a MIeffect prognoser 94, and a PTO effect prognoser 96. The monitor 84, forexample, includes the predictor 42 and the detector 44. The diagnosissystem 86, for example, includes the diagnoser 46.

The population prognoser 88 receives operational time data and generatesthe RUL therefrom. The MI and PTO cause prognosers 90, 92 receive timedata and causal data, such as vibration data, and predict the RUL forthe absorbed vibration energy. The MI and PTO effect prognosers 94, 96receive data generated by the diagnosis system 86, and calculate the RULtherefrom. In one embodiment, the MI and PTO effect prognosers 94, 96are trained to estimate the RUL for mud invasion (MI) and pressuretransducer offset (PTO) failures. In one embodiment, the MI and PTOeffect prognosers 94, 96 calculate the RUL from the cumulative sum ofthe fault alarms 64.

Although the cause and effect prognosers utilize MI and PTO faultclasses in generating the RUL, the system 80 is not limited to antspecific fault classes. Likewise, although the cause and effectprognosers are described in this embodiment as NFIS prognosers, theprognosers may utilize any suitable algorithm.

In one embodiment, to develop the population prognoser 88, data iscollected from a plurality of devices that are subject to normaloperating conditions or accelerated life testing, to extracttime-to-fail (TTF) information for each device. The cumulative TTFdistribution is then calculated. The first step in the development ofthe population prognoser 88 is to fit a probability density function(PDF) to the TTF data, such as the cumulative TTF distribution. In oneembodiment, to fit the data, a cumulative distribution function (CDF)associated with the PDF is estimated and the resulting estimates areused to estimate the parameters of a general distribution. Multiple PDFsmay be fit to the data via, for example, least squares, to determine thebest model for the failure times.

Other functions may be generated by the population prognoser 88. Forexample, the population prognoser 88 may use accelerated life testing orproportional hazards modeling to define the failure rate as a functionof time. In one embodiment, the proportional hazards model may also takeinto account various stressor variables in addition to time variables.

In one embodiment, an individual based prognoser is utilized todetermine the RUL. Examples of individual based prognosers include causeand effect prognosers 88, 90, 92, 94 and 96. The individual basedprognoser, in some examples, uses the GPM and produces RUL orreliability estimates. In embodiments that use the GPM, the devicedegradation is treated as an instantiation of a progression toward afailure threshold. Examples of algorithms that use the GPM includeCategorical Data Analysis, Life Consumption Modeling and ProportionalHazards Modeling, each of which produce either reliability estimates orRUL. Another example of an algorithm that uses the GPM includes variousextrapolation methods, which are used to produce the RUL. An example ofan algorithm that does not use the GPM is a Neural Network algorithm,which is used to produce the RUL.

In one embodiment, the individual based prognoser algorithms utilize thefollowing method. First, exemplar degradation paths are characterized bydetermining the “shape” of the path and a critical, failure threshold.The term “shape” refers to the parameter values of the degradationsignal and form of a physical model for various aspects of a device,such as the degradation, the parameters and the form of the functionregressed onto the path. In this embodiment, the exemplar degradationpaths need not be produced by example devices, but can be the product ofphysical models of the degradation mechanism. The failure threshold maybe set manually if known or can be inferred from the exemplar paths.

Next, the results of the path parameterization and threshold are used toconstruct an individual prognostic model. Finally, for a test device, toestimate the reliability (i.e., estimate a probability of failure) orRUL at some time t, the current progression of the test path ispresented as an input to the prognostic algorithm, which produces anestimate of the device reliability or RUL.

Various algorithms or models may be employed to parameterize theexemplar and measured degradation signals (e.g., environmental oroperational stress signals) to generate the degradation paths, and toestimate the RUL. Examples of such algorithms are described herein.

Categorical Data analysis (CDA) algorithms employ logistic regression tomap observed degradation parameters to one of two conditions, such as“no failure” (0) and “failure” (1). CDA uses logistic regression toestablish a relationship between a set of inputs (continuous orcategorical) to categorical outputs.

In this method, the probability of failure for an observation ofdegradation signals is estimated via a logistic regression model trainedon historical degradation data. For each degradation signal, there is anassociated critical threshold, and a failure is considered to haveoccurred when any one of the degradation signals crosses its associatedthreshold. This method provides a reliability estimate, but does notgenerate the RUL. In one embodiment, various time series analyses suchautoregressive moving average (ARMA) or curve fitting, are used toextrapolate the degradation signal to a future time where thereliability is zero or where the extrapolated path crosses the thresholdand hence estimate the RUL.

In proportional hazard (PH) modeling, the failure rate or hazardfunction depends on the current time as well as a series of stressorvariables that describe the environmental and operational stresses thata device is exposed to. Another example for estimating RUL is lifeconsumption modeling (LCM). In LCM, a new component begins its life withperfect health/reliability. As the device is used and/or exposed tovarious operating conditions, the health/reliability is deteriorated byamounts that are related to the damage absorbed by the device. Anexemplary LCM algorithm is accumulated damage modeling (ADM), which usesrough classes of stress conditions to estimate the increment by whichthe component health is degraded after each use. Another similarapproach is the cumulative wear (CW) model, which estimates the on-linereliability of a device by incrementally decreasing its reliability asit is used.

Extrapolation methods generally involve extrapolating the health of thedevice by using a priori knowledge and observations of historic deviceoperation. In general the extrapolation can be performed by either: 1)predicting future device stress conditions and then applying the stressconditions to a model of device degradation to estimate the RUL or 2)use trending techniques to extrapolate the path of the degradation orreliability signal to a failure threshold.

Various types of a priori knowledge can be used to estimate the futureenvironmental and operational conditions. This knowledge may take theform of multiple stress functions (i.e., stressors), each over aspecific time interval. For example, a deterministic sequence may beused if future stress levels and exposure times are known, byiteratively inputting the pre-determined stress levels and exposuretimes to a model of the device degradation to estimate the future healthof the device.

In population based probabilistic sequence methods, historical datacollected from a population of similar devices are used to estimateprobabilities for the incidence of specific stress levels and exposuretimes. In individual based probabilistic sequence methods, historicaldata collected from the individual device is used to estimate theprobabilities. To estimate the distribution of the RULs of a devicegiven its current state, simulations such as Monte Carlo simulations arerun in which the stress level and exposure times are sampled accordingto the estimated probabilities. Finally, the RUL for the individualdevice is estimated by taking the expected value of the resulting PDF ofthe RULs.

Other examples of prognostic algorithms include Fuzzy PrognosticAlgorithms such as Fuzzy Inference Systems (FIS) and Adaptive NeuralFuzzy Inference Systems (ANFIS). Various regression functions and neuralnetworks, and other analytical techniques may be used to estimate theRUL

The systems and methods described herein provide various advantages overprior art techniques. The systems and methods described herein aresimpler and less cumbersome than prior art techniques, which generallyemploy detailed physical models or cumbersome expert systems. Incontrast to methods that impose structure on the data through the use ofphysical models or detailed expert systems, the systems and methodsdescribed herein deriving structure from the data by allowing examplesto fully define the analysis components.

In addition, since the systems and methods described herein use datadriven techniques (i.e. data defines the model), the resulting systemsare easily automated and flexible enough to be adapted for changingdeployment requirements.

In support of the teachings herein, various analyses and/or analyticalcomponents may be used, including digital and/or analog systems. Thesystem may have components such as a processor, storage media, memory,input, output, communications link (wired, wireless, pulsed mud, opticalor other), user interfaces, software programs, signal processors(digital or analog) and other such components (such as resistors,capacitors, inductors and others) to provide for operation and analysesof the apparatus and methods disclosed herein in any of several mannerswell-appreciated in the art. It is considered that these teachings maybe, but need not be, implemented in conjunction with a set of computerexecutable instructions stored on a computer readable medium, includingmemory (ROMs, RAMs), optical (CD-ROMs), or magnetic (disks, harddrives), or any other type that when executed causes a computer toimplement the method of the present invention. These instructions mayprovide for equipment operation, control, data collection and analysisand other functions deemed relevant by a system designer, owner, user orother such personnel, in addition to the functions described in thisdisclosure.

Further, various other components may be included and called upon forproviding aspects of the teachings herein. For example, a sample line,sample storage, sample chamber, sample exhaust, pump, piston, powersupply (e.g., at least one of a generator, a remote supply and abattery), vacuum supply, pressure supply, refrigeration (i.e., cooling)unit or supply, heating component, motive force (such as a translationalforce, propulsional force or a rotational force), magnet, electromagnet,sensor, electrode, transmitter, receiver, transceiver, controller,optical unit, electrical unit or electromechanical unit may be includedin support of the various aspects discussed herein or in support ofother functions beyond this disclosure.

One skilled in the art will recognize that the various components ortechnologies may provide certain necessary or beneficial functionalityor features. Accordingly, these functions and features as may be neededin support of the appended claims and variations thereof, are recognizedas being inherently included as a part of the teachings herein and apart of the invention disclosed.

While the invention has been described with reference to exemplaryembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications will be appreciated by those skilled in theart to adapt a particular instrument, situation or material to theteachings of the invention without departing from the essential scopethereof. Therefore, it is intended that the invention not be limited tothe particular embodiment disclosed as the best mode contemplated forcarrying out this invention, but that the invention will include allembodiments falling within the scope of the appended claims.

1. A system for assessing a health of a borehole tool, the systemcomprising: at least one sensor associated with the borehole toolconfigured to obtain observation data relating to a characteristic of aformation; a memory in operable communication with the at least onesensor, the memory including a database configured to store theobservation data relating to the characteristic of the formation; and aprocessor in operable communication with the memory configured toreceive the observation data relating to the characteristic of theformation, the processor including: a detector receptive to theobservation data and configured to identify whether the borehole tool isoperating in a normal mode or a degraded mode, the degraded mode beingindicative of a fault in the borehole tool; a diagnoser responsive tothe observation data configured to identify a type of fault from atleast one symptom pattern; and a prognoser in operable communicationwith the at least one sensor, the detector and the diagnoser, theprognoser configured to calculate a remaining useful life (RUL) of theborehole tool based on a degradation path created from information fromat least one of the at least one sensor, the detector and the diagnoser,wherein the prognoser includes a population prognoser configured tocalculate the RUL based on a duration of use of the borehole tool, acause prognoser configured to calculate the RUL based on causal data,and an effect prognoser configured to calculate the RUL based on effectdata generated from the fault.
 2. The system of claim 1, furthercomprising a predictor in operable communication with the detector, thepredictor configured to receive one or more exemplar observations andcomparing the observation data to the one or more exemplar observationsto generate one or more estimated observations.
 3. The system of claim2, wherein the processor is configured to generate one or more residualobservations based on a comparison of the observation data and the oneor more estimated observations, and the detector is configured togenerate one or more exemplar residual observations and identify thedegraded mode based on a comparison between the one or more residualobservations and the one or more exemplar residual observations.
 4. Thesystem of claim 3, wherein the diagnoser is configured to identify thedegraded mode responsive to the one or more residual observationsexceeding a selected threshold.
 5. The system of claim 1, wherein theprocessor is configured to generate the symptom pattern for observationdata identified as representing the degraded mode, and the diagnoser isconfigured to generate one or more exemplar symptom patterns andidentify a type of the fault based on a comparison between the symptompattern and the one or more exemplar symptom patterns.
 6. The system ofclaim 1, wherein the processor is configured to generate a degradationpath and an associated lifetime for observation data identified asrepresenting the degraded mode, and the prognoser is configured toestimate the RUL based on a comparison of the degradation path and oneor more exemplar degradation paths.
 7. The system of claim 6, whereinthe prognoser is configured to estimate the RUL by identifying a closestexemplar degradation path associated with the type of fault at a time ofthe fault, calculating a time-to-failure of the closest exemplardegradation path, and subtracting the time of the fault from thetime-to-failure.
 8. The system of claim 1, wherein the detector, thediagnoser and the prognoser form a data-driven model.
 9. The system ofclaim 8, wherein the data-driven model is a nonparametric fuzzyinference system (NFIS).
 10. A method for assessing a health of aborehole tool, the method comprising: receiving observation datarelating to a characteristic of a formation obtained at at least onesensor associated with the borehole tool; and using a processor to:operate a detector to identify whether the borehole tool is operating ina normal mode or a degraded mode, the degraded mode being indicative ofa fault in the borehole tool, operate a diagnoser responsive to anidentification of the degraded mode to identify a type of fault from atleast one symptom pattern, create a degradation path using informationfrom the at least one of the sensor, the detector and the diagnoser, andoperate a prognoser to calculate a remaining useful life (RUL) of theborehole tool based on a comparison of the observation data with thecreated degradation path wherein calculating the RUL is based on: aduration of use of the borehole tool, causal data, and effect datagenerated from the fault.
 11. The method of claim 10, further comprisingreceiving one or more exemplar observations and comparing theobservation data to the one or more exemplar observations to generateone or more estimated observations.
 12. The method of claim 11, furthercomprising generating one or more residual observations based on acomparison of the observation data and the one or more estimatedobservations, generating one or more exemplar residual observations, andidentifying the degraded mode based on a comparison between the one ormore residual observations and the one or more exemplar residualobservations.
 13. The method of claim 12, further comprising identifyingthe degraded mode is responsive to the one or more residual observationsexceeding a selected threshold.
 14. The method of claim 10, furthercomprising generating the symptom pattern for observation dataidentified as representing the degraded mode, generating one or moreexemplar symptom patterns, and identifying the type of the fault basedon a comparison between the symptom pattern and the one or more exemplarsymptom patterns.
 15. The method of claim 10, wherein the processor isconfigured to generate a degradation path and an associated lifetime forobservation data identified as representing the degraded mode, and theprognoser is configured to estimate the remaining useful life based on acomparison of the degradation path and one or more exemplar degradationpaths.
 16. The method of claim 10, wherein calculating the RUL includesidentifying a closest exemplar degradation path associated with the typeof fault at a time of the fault, calculating a time-to-failure of theclosest exemplar degradation path, and subtracting the time of the faultfrom the time-to-failure.
 17. A non-transitory computer-readable mediumcontaining computer instructions stored therein for causing a computerprocessor to perform a method for assessing a health of a borehole tool,the method comprising: receiving observation data relating to acharacteristic of a formation at at least one sensor associated with theborehole tool; operating a detector to identify whether the boreholetool is operating in a normal mode or a degraded mode, the degraded modebeing indicative of a fault in the borehole tool; operating a diagnoserresponsive to an identification of the degraded mode to identify a typeof fault from at least one symptom pattern; creating a degradation pathusing information from the at least one of the sensor, the detector andthe diagnoser; and operating a prognoser to calculate a remaining usefullife (RUL) of the borehole tool based on a comparison of the observationdata with the created degradation path, wherein calculating the RUL isbased on: a duration of use of the borehole tool, causal data, andeffect data generated from the fault.
 18. The computer-readable mediumof claim 17, wherein the instructions further include: receiving one ormore exemplar observations and comparing the observation data to the oneor more exemplar observations to generate one or more estimatedobservations; generating one or more residual observations based on acomparison of the observation data and the one or more estimatedobservations; generating one or more exemplar residual observations; andidentifying the degraded mode based on a comparison between the one ormore residual observations and the one or more exemplar residualobservations.